Landslide erosion is a serious land management problem in many parts of the world, and especially in New Zealand where a combination of steep erodible hill country, a maritime climate featuring frequent and intense rainstorms, and recent forest clearance for pastoral farming have led to extensive landslide erosion on many parts of the country’s hill country farmland. Effective mitigation measures against landslide erosion and its consequences require a detailed understanding of the location, extent, and severity of landsliding. In New Zealand, this usually relies on detailed manual mapping from aerial photography [1
] and, more recently, spectral classification of regional satellite imagery following major storm events [2
]. For catchment- to farm-scale applications, however, manual image interpretation and mapping has to date been the most-used method for accurately identifying and mapping landsliding, but it is a very slow and tedious process and is thus limited to studies of relatively small areas and can be difficult to implement in practice. In addition, the quality of the resulting landslide maps depends on the experience of the investigator, the purpose of the mapping, the scale, and the data used [3
Just as the availability and quality of remote sensing data steadily increases, so too do the demands for extracting relevant geospatial information in a semi-automated or even fully automated manner. Optical remote sensing imagery constitutes a valuable and cost-effective source for the development of such classification techniques that offer the potential to significantly improve existing manual landslide mapping approaches, especially when combined with a degree of manual interpretation to create a “hybrid” approach to landslide identification. For example, landslide inventory maps can be prepared using such techniques [7
The selection of an appropriate technique depends on the purpose of the inventory, the size of the coverage, the time required by investigators, and the available resources [7
]. Conventional approaches for landslide recognition comprise resource- and time-consuming ground surveys and visual image interpretation using very high resolution (VHR) satellite or aerial photographs [11
] based on morphological appearance [13
]. High resolution (HR) imagery is used for delineating larger landslides [14
]. While visual interpretation is still the most common procedure for landslide mapping, recently, there has been a trend towards semi-automated landslide mapping approaches based on remote sensing data [15
]. Efficient image analysis techniques have opened a new era, particularly for studying denied-access, difficult-access, or remote sites [16
], but also for performing retrospective analysis based on historical images. Current approaches can basically be split into pixel-based and object-based categories [12
Over the last decade, object-based image analysis (OBIA) has been increasingly used for semi-automated landslide mapping using remote sensing data [6
]. OBIA, recently recognized as a new paradigm in remote sensing and Geographic Information Science [25
], enables researchers to work seamlessly with existing multi-scale geospatial data by combining image processing and GIS functionalities in one interlinked framework [26
]. OBIA allows the use of spectral, spatial, textural, contextual, and morphological properties. Geomorphological features such as landslides can be treated as aggregates of pixels and can be grouped into homogeneous objects, providing additional information on topological relationships of neighborhood, embeddedness, or shape [28
]. Unlike single pixels, image objects are enriched by a range of features/properties stemming from different data sources that can be used during classification. This is especially useful for VHR imagery, where objects of interest are usually significantly larger than the pixel size (H-resolution situation [29
]). Optical imagery is most often used in combination with a digital elevation model (DEM) and its derivatives such as slope or curvature. However, relatively few studies in literature so far have used aerial photographs for object-based landslide mapping [24
]. Even less research has been done for semi-automatically detecting landslides on panchromatic images, even though the creation of historical landslide inventory maps relies on the analysis of remote sensing data that has been acquired over the past few decades and is most often only available in black and white [23
]. A major reason therefore is that the limited spectral information of panchromatic images hampers the differentiation of classes and the detection of features of interest. This is particularly true for semi-automated methods, since they mainly rely on thresholds derived from multispectral bands [23
]. However, brightness thresholds from panchromatic images can be used for detecting landslide-affected areas, since these areas tend to appear brighter due to a loss of vegetation and the exposure of fresh rock and bare soil [33
Manually or semi-automatically mapped landslides from optical images can be used as input for creating landslide hotspot (or density) maps, which are ideal for an easy-to-grasp visual representation of the worst landslide-affected areas after a triggering event (e.g., as produced by the Earthquakes without Frontiers project following the earthquake in Nepal in April 2015 [34
]). A few studies investigated the spatial patterns of PSI (Persistent Scatterer Interferometry) point targets for slow moving landslides using synthetic aperture radar (SAR) data complemented by landslide inventories for the identification of landslide hotspots [35
]. Landslide hotspot maps are also valuable when planning field surveys and in situ validation campaigns, so that field work can be prioritized and the time and effort needed significantly reduced [35
In this study we aim to identify spatio-temporal landslide hotspots by analyzing historical and recent aerial photography for a landslide-prone study site in New Zealand. Landslide hotspots are calculated based on the distribution of semi-automatically detected landslides using OBIA, and compared to hotspots derived from manually mapped landslides.
Visual interpretation enables landslide scars and debris tails to be differentiated. It is worth noting that fewer landslide tails than scars were mapped, because tails had frequently grassed over prior to aerial photography being captured, whereas scars tend to take longer to revegetate owing to a lack of remaining soil on the scar faces. On the other hand, the cumulative area covered by landslide scars was considerably less than that covered by tails, reflecting the fact that sediment generated from even small scars is often spread over a relatively large downslope area.
Such a differentiation of landslide scars and debris tails could not be done with OBIA, because the debris tails did not show distinct spectral characteristics that would have facilitated their reliable detection. Semi-automated (OBIA) mapping results would be sensitive to the length of time elapsed between a landslide triggering event and the next available image (Table 3
). Imagery taken immediately after a heavy rainfall is ideal for capturing the full extent of landslides, and is a significant reason for the large area of landslides mapped in 2005 following the storm event in 2004. In contrast, no large rainfall event precedes the 2011 photography, which may indicate that old landslide scars from the 2004 event have been captured. An accurate differentiation of the actual source areas (scars) and deposition areas (tails) by OBIA may be facilitated with additional imagery taken a number of weeks after an event when the depositional tails will have started to revegetate, whilst the scars would still be clearly visible.
The manual mapping approach shows advantages over the semi-automated method for delineating single landslides or splitting up compound landslide complexes into separate landslides [43
]. Since segmentation-derived image objects rarely correspond to single landslides due to over- or undersegmentation and the variability inherent to landslides, the exact demarcation of objects that represent single landslides is a major challenge and further research is needed in this direction. Advanced algorithms for object boundary refinement (e.g., split and merge based on specific conditions) could be used to improve the delineation of single landslides. However, the creation of “meaningful” objects with regard to a particular context or aim can be very complex [25
]. Thus, only landslide-affected areas and not the number of landslides were mapped by OBIA.
Guzzetti et al. [7
] review conventional methods for the production of landslide inventory maps and examine the role of new techniques based on modern technologies. The review discusses the value and limitations of geomorphological field mapping and the interpretation of stereoscopic aerial photographs and evaluates semi-automatic approaches for the recognition of landslides, including the OBIA method. Guzzetti et al. [7
] conclude that the systematic use of semi-automatic techniques limits subjectivity and can contribute to improving the reproducibility of landslide maps.
Manual mapping, while generally more accurate than semi-automated methods, is also a very time-consuming process, and any large-scale mapping task would presumably benefit from a degree of automation [30
]. A hybrid approach that combines both semi-automated feature delineation and manual interpretation could improve the whole mapping process and can lead to acceptably accurate mapping results with the potential to greatly reduce the time and effort needed for generating landslide inventories. For example, the initial delineation of areas of bare ground (i.e., the rapid identification of target areas) could be automated, followed by a manual refinement by an experienced interpreter.
The produced landslide hotspot maps show that the distribution of the manually identified landslides and those mapped with OBIA is very similar for all periods. Hotspot maps have the advantage that areas most affected by landslides can be immediately identified, even if the accuracy of the actually mapped landslides is limited. Hotspots maps created on the basis of OBIA mapping results can be produced very quickly and thus might be particularly valuable for the rapid information provision after landslide triggering events.